WO 2011/021128 A2 discloses a method and a system for image analysis, including:
obtaining a sequence of images;
performing a vision-based analysis on at least one of the sequence of images to obtain data for classifying a state of a subject represented in the images;
determining at least one value of a physiological parameter of a living being represented in at least some of the sequence of images, wherein the at least one value of the physiological parameter is determined through analysis of image data from the same sequence of images from which the at least one image on which the vision-based analysis is performed is taken; and
classifying a state of the subject using the data obtained with the vision-based analysis and the at least one value of the physiological parameter.
The document further discloses several refinements of the method and system. For instance, the use of remote photoplethysmographic (PPG) analysis is envisaged. In general, in the field of image processing enormous progress was made in that profound analyses of the recorded data were enabled. In this context, it could be envisaged to extract information from recorded data in a way so as to enable detailed conclusions regarding the physical condition or even the well-being of an observed living individual.
WO 2011/042858 A1 discloses a further method and system addressing processing a signal including at least a component representative of a periodic phenomenon in a living being. Additional basic approaches to remote photoplethysmography are described in Verkruysse, W. et al (2008), “Remote plethysmographic imaging using ambient light” in Optics Express, Optical Society of America, Washington, D.C., USA, vol. 16, no. 26, pp. 21434-21445.
However, the recorded data, such as captured reflected or emitted electromagnetic radiation, especially recorded image frames, always comprises, beside of the desired signal to be extracted therefrom, further signal components deriving from overall disturbances, by way of example, such as noise due to changing luminance conditions or a movement of observed objects. Hence, a detailed precise extraction of the desired signals still poses major challenges for the processing of such data.
Although considerable progress in the field of computing performance has been made, it is still a challenge to provide for instant image recognition and image processing enabling immediate, so to say, on-line detection of desired vital signals. This applies in particular to mobile device applications commonly lacking of sufficient computing power. Furthermore, data transmission capacity can be restricted in several applications.
A possible approach to this challenge may be directed to providing well-prepared and steady ambient conditions when capturing a signal of interest in which the desired signal component is embedded so as to minimize disturbing signal components overlaying the signal. However, such laboratory conditions cannot be transferred to everyday field applications as high efforts and preparation work would be required therefor.
After all, vital signal detection is made even more difficult when amplitudes and/or nominal values of disturbing signal components are much larger than amplitudes and/or nominal values of desired signal components to be extracted. Potentially, the magnitude of difference between the respective components can be expected to even comprise several orders.